Quantum computing is fast-growing and progressive technology that provides the best results in various domains – including healthcare. In this paper, the Quantum Machine Learning (QML) algorithms, namely Quantum Support Vector Classifiers (QSVC) and Quantum Convolutional Neural Networks (QCNN), are employed for classifying cardiovascular diseases using the Cardiovascular dataset from Kaggle. In contrast with conventional models, the QML algorithms make explicit use of superposition and entanglement to compress and process highly complex and high-dimensional data. In the QSVC approach, patient data are transformed into quantum space with higher-dimensional features by quantum feature maps. This transformation aids QSVC in the process of identifying the complex patterns which are missed in traditional ML methods. In QCNN, the quantum circuits are combined with convolutional layers to capture the understandable features and improve its ability in classification. Both methods are deployed on performance metrics such as Accuracy, Precision, Recall, F1-score and AUC-ROC to show the potential of the methods. The accuracy of the produced QCNN (92%) is greater than that of QSVC (87%). The quantum models demonstrate superior performance regarding metrics and precision level as a result of their rapid convergence. A medical diagnostic revolution occurs through QML algorithms enabling classification between healthy and unhealthy cardiovascular patterns.
Quantum computing is fast-growing and progressive technology that provides the best results in various domains – including healthcare. In this paper, the Quantum Machine Learning (QML) algorithms, namely Quantum Support Vector Classifiers (QSVC) and Quantum Convolutional Neural Networks (QCNN), are employed for classifying cardiovascular diseases using the Cardiovascular dataset from Kaggle. In contrast with conventional models, the QML algorithms make explicit use of superposition and entanglement to compress and process highly complex and high-dimensional data. In the QSVC approach, patient data are transformed into quantum space with higher-dimensional features by quantum feature maps. This transformation aids QSVC in the process of identifying the complex patterns which are missed in traditional ML methods. In QCNN, the quantum circuits are combined with convolutional layers to capture the understandable features and improve its ability in classification. Both methods are deployed on performance metrics such as Accuracy, Precision, Recall, F1-score and AUC-ROC to show the potential of the methods. The accuracy of the produced QCNN (92%) is greater than that of QSVC (87%). The quantum models demonstrate superior performance regarding metrics and precision level as a result of their rapid convergence. A medical diagnostic revolution occurs through QML algorithms enabling classification between healthy and unhealthy cardiovascular patterns.
Cardiovascular disease is one of the most common reasons of increases the global death rate, suggesting initial detection and timely treatment crucial. While traditional machine learning models have been effective in predicting heart disease, Quantum Computing (QC) introduces new possibilities for improving accuracy and processing speed. In this regard the application of Quantum Machine Learning (QML) approaches-categorically, Quantum Neural Networks (QNN) and Quantum Support Vector Classification (QSVC)-to predict heart disease using key health indicators such as present-age, levels of cholesterol, Blood Pressure (BP), and lifestyle choices. QNN is utilized to recognize complex patterns in patient data, leveraging deep learning to understand intricate connections between various risk factors. Meanwhile, QSVC takes advantage of QC ability to compute large amounts of information at once, making classification faster and more precise. Both models are trained and tested on a heart disease dataset and compared with traditional machine learning methods to assess their accuracy, efficiency, and scalability. The results show that while quantum and classical models achieve similar accuracy, quantum computing significantly reduces processing time, making it highly efficient for analyzing large datasets. This study highlights the strength of QC in transforming medical diagnostics, offering faster and more reliable predictive models for early heart disease detection. By integrating quantum algorithms into healthcare, this research contributes to the advancement of AI-driven medical solutions, resulting in more efficient and individual patient care.
This research is the first of its kind to leverage the power of Quantum Machine Learning (QML) to perform multi-class classification of Cardiovascular Diseases (CVDs). We propose a novel approach that enables multi-class classification with Pegasos Quantum Support Vector Classifier (QSVC). The QSVC and the Pegasos QSVC significantly outperform the classical SVC by a margin of +10.76% and +9.72%, respectively. The paper further ventures into a quantum deep learning based architecture with a novel Quanvolutional Neural Network (QNN) implementation, outperforming the other models by achieving 97.31% accuracy, 97.41% precision, 97.31% recall, 97.30% F1 score, and 99.10% specificity.
Abstract Accurate early identification and treatment of cardiovascular diseases can prevent heart failure problems and reduce mortality rates. This study aims to use quantum learning to predict heart problems to increase the accuracy of traditional prediction and classification methods. Machine learning (ML) and deep learning (DL) techniques need quantum learning to quickly and accurately analyze massive volumes of complex data. With quantum computing, the suggested DL and ML algorithms can change their predictions on the basis of changes in the dataset. This approach could help with the early and accurate detection of chronic diseases. The Cleveland heart disease dataset is undergoing preliminary processing to validate missing values to increase the precision rate and prevent incorrect forecasts. This study examined the feasibility of employing and deploying a quantum ML (QML) framework via cloud computing to categorize cardiac conditions. The research was divided into four sections. First, the principal component analysis was used to preprocess the Cleveland dataset, recursive feature elimination was used to select features, and min–max normalization was used to give the dataset a high-dimensional value. Second, we compared traditional classifiers, such as support vector machine (SVM) and artificial neural network, with the quantum approach to verify the quantum approach’s efficiency. Third, we examined two unique QML classification methods: quantum neural networks (QNNs) and quantum SVM (QSVM). Fourth, bagging-QSVM was developed and deployed as an ensemble learning model. Experimental results using the QNN show an accuracy of 77%, a precision of 76%, a recall of 73%, and an F 1 score of 75%. With an accuracy of 85%, a precision of 79%, a recall of 90%, and an F 1-score of 84%, the QSVM method demonstrated a much better performance than the QNN. Particularly, the Bagging_QSVM model exhibited an outstanding performance, with a flawless score of 100% across all critical performance measures. The study shows that the bagging method for ensemble learning is a solid way of increasing the accuracy of quantum method predictions.
This research presents a novel quantum-hybrid machine learning framework for heart disease classification that integrates Quantum Neural Networks with variational circuits and Quantum Support Vector Machines featuring custom quantum kernels alongside classical ensemble methods. The methodology employs advanced feature engineering, multi-strategy missing value imputation, and combined feature selection techniques on an enhanced dataset of 2,000 cardiovascular cases with 17 engineered features. The quantum components utilize 8-10 qubits across 5-6 variational layers with quantum interference effects for enhanced pattern recognition capabilities. Through weighted soft voting ensemble strategy and dynamic threshold optimization, the quantum-hybrid system achieved exceptional performance metrics including 96.2% accuracy, 95.8% F1-score, 94.7% precision, and 96.9% recall, significantly exceeding the 95% accuracy target. The quantum models demonstrated measurable advantages over classical approaches, with the Quantum Neural Network reaching 92.4% accuracy and Quantum SVM achieving 90.8% accuracy independently. The hybrid ensemble showed 4.3 percentage points improvement over the best individual classical model and 2.7 percentage points over standalone quantum implementations. This quantum-hybrid approach establishes superior diagnostic capabilities for cardiovascular disease prediction, particularly in minimizing false negatives critical for patient safety, positioning quantum-enhanced machine learning as a viable solution for next-generation medical diagnostic systems with clinically relevant accuracy improvements for healthcare applications.
Abstract Quantum machine learning (QML) is an emerging field that combines the power of quantum computing with machine learning (ML) techniques to solve complex problems. In recent years, QML algorithms have shown tremendous potential in various applications such as image recognition, natural language processing, health care, finance, and drug discovery. QML algorithms aim to reduce computation costs and solve complex problems beyond the scope of classical machine learning algorithms. In this article, we study the performance of two QML algorithms, that is, quantum support vector classifiers (QSVC) and variational quantum classifiers (VQC), for chronic heart disease prediction in Healthcare 4.0. The performance of the two classifiers is assessed using different evaluation metrics like accuracy, precision, recall, and F1 score. The authors concluded the superior performance of QSVC over VQC with an accuracy of 82%.
Cardiovascular disease remains one of the leading causes of death worldwide, making early and accurate risk prediction essential. Conventional machine learning models often struggle to handle complex medical data, resulting in limited accuracy and reduced interpretability. To address these challenges, this study presents a hybrid prediction framework that combines Quantum Machine Learning (QML) with ensemble learning techniques. Classical models such as Support Vector Classifier (SVC) and Artificial Neural Network (ANN) are used as baseline approaches. In addition, quantum models including QSVC, Variational Quantum Classifier (VQC), and Quantum Neural Network (QNN) are employed to capture complex non-linear patterns present in clinical data. To further enhance performance, a Bagging-QSVC ensemble model is proposed, improving robustness and overall classification accuracy. Interpretability is incorporated using SHAP, allowing the model’s predictions to be more transparent and easier to understand in a clinical context. Experimental results indicate that the proposed quantum ensemble achieves an accuracy of around 90%, outperforming both classical models and individual quantum models. Furthermore, a real-time user interface is developed, enabling clinicians to input patient data and receive immediate risk predictions. Overall, the system provides a reliable and interpretable solution suitable for practical clinical decision support.